def test_different_config_different_results(self):
inputs = tf.get_variable(name='inputs', shape=[50, 100])
with tf.variable_scope('a'):
conf_a = lsh.get_simhash_config(100, 8)
with tf.variable_scope('b'):
conf_b = lsh.get_simhash_config(100, 8)
hash_a = lsh.simhash(inputs, conf_a)
hash_b = lsh.simhash(inputs, conf_b)
equal = tf.reduce_all(tf.equal(hash_a, hash_b))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertFalse(sess.run(equal))
def test_one_at_a_time(self):
"""make sure if gives a scalr back if given a vector"""
inputs = tf.random_normal([5])
conf = lsh.get_simhash_config(5, 2)
hashed = lsh.simhash(inputs, conf)
self.assertEqual(hashed.get_shape().as_list(), [])
def test_shapes(self):
"""just ensure we get the correct shapes back"""
inputs = tf.random_normal([10, 15])
conf = lsh.get_simhash_config(15, 16)
hashed = lsh.simhash(inputs, conf)
self.assertEqual([10, 1], hashed.get_shape().as_list())
def _get_bucket(self, key):
"""look up the contents of a bucket by hash. Also return the bucket
index so we can create updates to the storage variables."""
idx = simhash(key, self._hash_config)
bucket_start = idx * self._bucket_size
bucket_end = (idx + 1) * self._bucket_size
keys = self._keys[bucket_start:bucket_end, ...]
values = [val[bucket_start:bucket_end, ...] for val in self._values]
return keys, values, idx
def test_deterministic(self):
"""ensure it gives the same result twice"""
inputs = tf.get_variable(name='inputs', shape=[1, 20])
conf = lsh.get_simhash_config(20, 8)
hashed = lsh.simhash(inputs, conf)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
a = sess.run(hashed)
b = sess.run(hashed)
self.assertEqual(a, b)
def test_max_bits(self):
"""not an exhaustive test"""
inputs = tf.random_normal([10, 100])
conf = lsh.get_simhash_config(100, 4)
hashed = lsh.simhash(inputs, conf)
in_range = tf.reduce_all(tf.less(hashed, 2**4))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(100):
self.assertTrue(sess.run(in_range))
